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Improved estimation of canopy water status in cotton using vegetation indices along with textural information from UAV-based multispectral images

文献类型： 外文期刊

作者： Pei, Shengzhao 1 ; Dai, Yulong 1 ; Bai, Zhentao 1 ; Li, Zhijun 1 ; Zhang, Fucang 1 ; Yin, Feihu 1 ; Fan, Junliang 1 ;

作者机构： 1.Northwest A&F Univ, Minist Educ, Key Lab Agr Soil & Water Engn Arid & Semiarid Area, Yangling 712100, Shaanxi, Peoples R China

2.Xinjiang Acad Agr & Reclamat Sci, Inst Farmland Water Conservancy & Soil Fertilizer, Shihezi 832000, Xinjiang, Peoples R China

关键词： Unmanned aerial vehicle; Canopy equivalent water thickness; Crop water stress index; Vegetation indices; Image texture

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 224 卷

页码：

收录情况： SCI

摘要： Precise and timely estimation of crop canopy water status is of great importance for precision irrigation. The unmanned aerial vehicle (UAV)-based remote sensing technology has become increasingly popular for crop canopy water status estimation. Nevertheless, the capability of vegetation indices (VIs) along with textural information from high -resolution imagery for estimating cotton canopy water status has been rarely explored. The VIs, texture features (TFs), and texture indices (TIs) were obtained from UAV multispectral images of cotton with different irrigation levels and nitrogen rates in the southern Xinjiang of China. The performances of three machine learning models, i.e. support vector machine (SVM), back -propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were evaluated for estimating canopy equivalent water thickness (CEWT) and crop water stress index (CWSI) throughout the cotton growing season. The results showed that combining vegetation indices and textural information significantly improved the estimation accuracy of models compared to vegetation indices or textural information alone. The XGBoost_VIs + TFs model exhibited the best accuracy in estimating CEWT (R 2 = 0.75, RMSE = 0.01 cm, RE = 19.46 % at upper half -canopy level, and R 2 = 0.65, RMSE = 0.02 cm, RE = 24.59 % at all -canopy level), while the XGBoost_VIs + TFs + TIs model performed best in predicting CWSI among the models (R 2 = 0.90, RMSE = 0.05, RE = 5.84 %). Although CEWT estimation was fair to some extent, CWSI estimation was more applicable for diagnosing cotton water stress. The CWSI maps created from the optimal XGBoost_VIs + TFs + TIs model intuitively reflected the cotton canopy water status under various irrigation levels and nitrogen rates, which could help farmers implement timely and precision irrigation in cotton production.

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